Deskpro to JSM migration requires API-based extraction to preserve ticket threads, attachments, and internal notes. CSV flattens everything. Plan for HTML-to-ADF conversion and per-issue limits.
There is no native migration path from Deskpro to Jira Service Management; every approach requires extracting data from Deskpro and transforming it to fit JSM's fundamentally different data model. The core challenge lies in converting Deskpro's HTML-threaded ticket messages into Atlassian Document Format (ADF) for JSM's REST API v3, and resolving Deskpro's email-based People records to Jira accountIds. CSV export captures only flat ticket metadata—stripping conversation threads, attachments, and customer–organization relationships—so API-based migration is required for any production environment that needs full data fidelity.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Quick Answer
Migrating from Deskpro to Jira Service Management (JSM) is a data-model translation, not a file copy. The biggest technical hurdles are converting Deskpro's HTML message bodies into the Atlassian Document Format (ADF) required by JSM's REST API v3, and resolving email-based Deskpro users to Jira accountIds. CSV export captures ticket metadata but strips conversation threads, attachments, and customer–organization relationships. For anything beyond basic ticket data, API-based migration is the only method that preserves full fidelity.
If the target Atlassian Cloud site was created after October 10, 2025, richer customer
If the target Atlassian Cloud site was created after October 10, 2025, richer customer and organization profile features live in Customer Service Management, not only inside Jira Service Management. Plan your company/contact mapping around that date boundary. (support.atlassian.com)
HTML-to-ADF conversion is a common failure point
Jira's v3 API expects Atlassian Document Format (a JSON structure), not raw HTML. If you POST HTML as a comment body, it renders as raw markup. Options: use the v2 API (/rest/api/2/) which accepts wiki notation or plain text, convert HTML to ADF using Atlassian's @atlaskit/editor-json-transformer library, or strip HTML to plain text (lossy but simple). Nested tables, inline styles, and complex blockquotes in Deskpro HTML will cause standard converters to fail with HTTP 400 errors. You must build a robust parser or accept lossy conversion.
Author attribution
If you create all comments using a single admin API key, every comment appears as the admin. To preserve original authorship, you need separate API tokens per agent or use Atlassian's impersonation (available only on certain tiers). Jira Cloud falls back to the importing user if the comment author does not already exist on the site with the right permissions. (support.atlassian.com) This is the single most common fidelity loss in helpdesk migrations.
The runbook
Work top to bottom. Tick steps as you go — your progress is saved in this browser.
01 Discovery Establish why you are moving, what "done" means, and who signs off.
Objective A written scope with agreed success criteria, a named owner per workstream, and a budget approved by finance.
Keep these open
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Pull the real numbers out of Deskpro
Export counts for tickets (open and closed separately), contacts, organisations, attachments, macros, triggers, automations, views and SLA policies. Note the oldest ticket date — history depth drives the whole timeline. Estimating from memory is the single most common cause of a blown migration window.
Data Profiler Get real record counts instead of estimating from memory -
Decide what history actually moves
Agree a cut-off with the support lead: all history, last 24 months, or open tickets plus a read-only archive. Every extra year of closed tickets adds API time and cost without adding much agent value. Get this in writing — it is the decision people relitigate mid-cutover.
A "move everything" default is what turns a two-week migration into a two-month one.
COI & ROI Calculator Build the 36-month business case you will need for sign-off -
Confirm Jira Service Management can hold your support model
Walk your current workflow through Jira Service Management: multi-brand, business hours, SLA targets, CSAT, side conversations, public vs internal notes, and any channel you depend on (voice, chat, WhatsApp, social). List anything with no native equivalent — those are project risks, not configuration details.
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Build the business case
Model licence delta, migration effort, agent retraining, and the cost of staying put (Cost of Inaction). Executives approve a number, not a plan, and you will be asked for it again at the go/no-go.
Helpdesk Migration Planner Turn ticket volume into a dated Deskpro → Jira Service Management timeline -
Name owners and set the go/no-go date
One named owner each for data, configuration, integrations, and agent enablement, plus a decision-maker who can call a rollback. Put the go/no-go meeting in calendars now, 48 hours before the freeze.
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Retry overhead
A migration that fails at record 15,000 of 50,000 due to an unhandled edge case requires re-engineering and re-running — not just a restart.
Deskpro → Jira Service Management specifics
- Atlassian ecosystem consolidation
- Teams already using Jira Software and Confluence want a single vendor for engineering and support.
- ITSM maturity
- JSM provides native ITIL workflows — incident, change, problem management — that Deskpro doesn't offer out of the box.
- Asset management
- JSM's built-in Assets (formerly Insight) provides CMDB capabilities for IT teams.
- Automation and integration depth
- JSM's automation engine and the Atlassian Marketplace provide broader integration options for teams scaling beyond pure helpdesk.
- Cost restructuring
- Teams with specific agent-count and feature requirements may find JSM's pricing tiers more favorable.
Don't move on until
- Record counts confirmed for tickets, contacts, organisations and macros
- Success criteria signed off by the support lead
- Freeze window provisionally booked with the business
02 Data Audit Find out what is actually in the data before you try to move it.
Objective A profiled, cleaned export with every quality defect either fixed at source or explicitly accepted.
Keep these open
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Take a full Deskpro export and profile it
Export to CSV or JSON and profile every file: row counts, null rates per column, distinct values, and type consistency. Compare row counts against the API totals from Discovery — a gap here means your export is silently truncated, usually by pagination.
Data Profiler Profile the Deskpro export for nulls, outliers and type drift -
Validate file structure before anyone writes a transform
Check delimiters, quoting, encoding (expect UTF-8, watch for BOMs and Latin-1), duplicate headers, and embedded newlines in ticket bodies. Ticket descriptions with raw newlines and commas break naive CSV parsers and silently shift columns.
A single unescaped quote in one ticket body can shift every subsequent column without any error.
CSV Validator Catch broken headers and ragged rows in the raw export -
Inventory PII and set retention
Scan for emails, phone numbers, payment card fragments, national IDs and anything else regulated in ticket bodies and custom fields — support tickets are where customers paste things they should not. Decide what gets migrated, masked, or dropped, and record the legal basis.
Ticket bodies and attachments routinely contain card and ID data that never appears in a structured field.
PII & Compliance Scanner Find regulated fields before they land in a new system -
Quantify duplicates, orphans and dead references
Count duplicate contacts (same email, different casing), tickets whose requester no longer exists, organisations with no members, and attachments whose parent ticket is gone. Fix these in Deskpro where you can — migrating them just moves the mess.
Data Cleaner Strip empty rows, stray whitespace and dead columns -
Clean and normalise the export
Trim whitespace, drop empty rows and columns, normalise casing on emails and tags, and standardise every timestamp to UTC ISO 8601. Timezone drift is invisible at load time and shows up weeks later as SLA reports nobody can reconcile.
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Produce a masked copy for sandbox work
Generate a realistic but fake version of the export for testing and for any vendor who needs sample data. Loading real customer PII into a sandbox is a breach in most jurisdictions, and sandboxes are rarely covered by your DPA.
PII Masker Generate a safe copy for sandbox and vendor testing
Deskpro → Jira Service Management specifics
- Ticket count
- by status (open, resolved, closed, archived), age, department, and channel
- Knowledge Base articles
- count and structure (this is a separate migration to Confluence)
- CRM-style data
- any accounts, contacts, or custom objects modeled through Deskpro custom fields or apps. These are not native Deskpro helpdesk objects; inventory them and decide whether they belong in JSM fields, customer/organization profiles, Assets, or a separate system.
- Rate limits
- Cloud instances have a default global rate limit. When you hit a rate limit, requests will begin to fail with an HTTP status code 429 Too Many Requests error. Contact Deskpro support to raise limits before a migration.
- No bulk export API
- Every record must be fetched through paginated list endpoints. There's no bulk archive download.
Don't move on until
- Export parses cleanly with no ragged rows or encoding errors
- PII inventory complete and retention decisions recorded
- Duplicate and orphan records quantified and triaged
03 Field Mapping Turn two schemas into one signed-off mapping spec.
Objective A reviewed field-level mapping covering every object, with an explicit decision for every field that has no target.
Keep these open
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Generate the first-pass Deskpro → Jira Service Management field map
Start from an automated match on both schemas, then review every row by hand. Automated matching gets the obvious 70% right and is confidently wrong on the rest — especially anything named "type", "status" or "custom_field_1".
Schema Mapper Opens pre-loaded with the Deskpro → Jira Service Management field pair -
Map status, priority and channel values, not just field names
Enumerate every value in each picklist on both sides and map them explicitly. Value-level mismatches are the defect class that survives all the way to production because the field itself mapped fine — a ticket that should be "Pending" arriving as "Open" reopens SLA clocks.
Statuses with no target equivalent (on-hold, pending-customer) need a policy decision, not a best guess.
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Decide how custom fields land
Create the target custom fields first, matching type exactly (a dropdown mapped to free text can never be mapped back). Where Jira Service Management has no equivalent, decide between a new custom field, a tag, or a note appended to the ticket body — and record which.
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Resolve identity and threading
Decide how source IDs are preserved — most platforms will not let you set the primary key, so keep the original ID in a custom field. Without it, reconciliation becomes fuzzy matching and every future support question about an old ticket is unanswerable.
Losing the original ticket ID makes reconciliation and rollback effectively impossible.
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Plan attachments, inline images and threading order
Confirm size limits, allowed MIME types, and whether inline images survive as attachments or need rehosting. Decide the comment ordering and author attribution rules: comments loaded out of order, or all attributed to the API user, destroy the conversation history agents rely on.
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Freeze and sign off the mapping spec
Version the spec, walk the support lead through it row by row, and get explicit sign-off. Any change after this point goes through change control — mid-flight mapping edits are how partial loads happen.
Deskpro → Jira Service Management specifics
- Custom field mapping
- including Deskpro's per-department custom fields to JSM's project-scoped fields
- Custom fields
- list all ticket, person, and org custom fields with their types
Don't move on until
- Every source field is mapped, deliberately dropped, or parked in a custom field
- Status, priority and channel value maps agreed with the support lead
- Mapping spec version-controlled and signed off
04 Test Migration Prove the pipeline on a small, representative slice.
Objective A pilot load into a Jira Service Management sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Jira Service Management sandbox that matches production config
Create the custom fields, groups, brands, business hours and SLA policies first. A pilot into a default sandbox tests nothing, because the failures you care about are all configuration mismatches.
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Pick a deliberately nasty pilot sample
Take 500-1000 records chosen for difficulty, not convenience: the longest ticket threads, tickets with the most attachments, non-Latin character sets, merged and split tickets, deleted requesters, and every status value. A clean random sample proves only that easy records are easy.
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Run the load with masked data and instrument everything
Log every API request and response with its source record ID. When 40 records fail out of 10,000 you need to know exactly which ones and why, without re-running the whole batch.
PII Masker Never load real customer PII into a sandbox -
Measure real throughput against the rate limit
Record achieved records-per-hour under Jira Service Management's actual rate limits, including retries and backoff. Extrapolate to the full volume: if the maths says the full load exceeds your freeze window, you fix that now, not on cutover night.
Published rate limits are ceilings, not throughput. Assume real-world rates are meaningfully lower once retries and backoff are counted.
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Reconcile the pilot and triage every failure
Diff source against target on record counts and field-level values. Every discrepancy gets a root cause and a fix — "probably fine" at pilot scale becomes thousands of broken records at full scale.
Migration Validation Tool Diff the pilot batch against source before scaling up -
Put real agents in front of the pilot data
Have two or three agents work sample tickets end to end in the sandbox. They find the things reconciliation cannot see: unreadable threading, missing context, macros that no longer make sense. Fix the mapping, then re-run.
Don't move on until
- Pilot batch reconciles to 100% on record counts
- Agents have reviewed sample tickets and confirmed they are workable
- Measured throughput extrapolates to a viable full-load window
05 Cutover Execute the switch inside a controlled, reversible window.
Objective All in-scope data live in Jira Service Management, agents working in the new system, and a rollback path that stayed available throughout.
Keep these open
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Pre-load history before the freeze
Load closed tickets and contacts days or weeks ahead while Deskpro stays live. Only open tickets and the final delta need to move inside the freeze — this is the single biggest lever on window length.
Helpdesk Migration Planner Size the freeze window from Jira Service Management's real API limits -
Publish the runbook with times, owners and abort criteria
A timed sequence: freeze start, final export, delta load, channel switch, smoke test, go/no-go, agent switch. Name who does each step and the explicit condition that triggers a rollback. Decide the abort criteria before the night, when nobody wants to be the one to call it.
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Freeze Deskpro and take the final delta
Stop new ticket creation, let agents finish in-flight replies, then export everything changed since the pre-load. Announce the freeze to the whole business, not just support — someone always tries to raise a ticket during it.
Tickets created during an unenforced freeze land in the old system and are the most common source of permanently lost data.
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Load the delta and open tickets
Run the delta load, then reconcile counts before touching any channel. Do not repoint email until the delta has verified — an inbound ticket arriving mid-load is far harder to untangle than a few extra minutes of freeze.
Migration Validation Tool Confirm the final delta landed before you reopen -
Repoint channels and verify with live traffic
Switch email forwarding and MX or connector settings, update chat widgets and web forms, and re-authorise integrations. Then send real test tickets through every channel and confirm each lands, routes and triggers the right automation.
Email forwarding changes can take up to a full DNS TTL to propagate — check the TTL days in advance and lower it if needed.
Cron Expression Builder Schedule the delta syncs that run through the freeze -
Run the go/no-go and switch the agents
Walk the exit criteria with the decision-maker, call it explicitly, then move agents over with a named person on hand for the first few hours. Keep Deskpro read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Deskpro → Jira Service Management specifics
- JSM Cloud
- Bulk-delete migrated issues using JQL + bulk operations or the API
- JSM Data Center
- Restore from a pre-migration database backup
- Always keep Deskpro running
- until JSM is fully validated — don't cancel your Deskpro subscription until agents have worked in JSM for at least 2 weeks
Don't move on until
- Full historical load complete and counts matched
- Inbound channels repointed and verified with live test tickets
- Rollback decision point passed explicitly, not by default
06 Validation Prove the migration is complete, then close it out.
Objective Documented evidence that data, workflow and reporting all survived, and a signed acceptance.
Keep these open
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Run the full reconciliation
Compare source and target on every object: total counts, counts by status, counts by group, attachment counts, and field-level spot checks on a random sample. Produce one report you can hand to an auditor.
Migration Validation Tool Reconcile Deskpro and Jira Service Management record-for-record -
Verify field completeness, not just record counts
Re-profile the loaded data and compare null rates per field against the source profile. Matching record counts with a field that silently arrived empty is the failure mode counts alone will never catch.
Data Profiler Prove field completeness held up through the load -
Rebuild reporting and compare against baselines
Recreate your core dashboards — volume, first response time, resolution time, CSAT — and compare to pre-migration figures for the same period. Explain every variance; a changed SLA calculation is a real finding, not a rounding error.
SLA and first-response metrics are usually recalculated from the loaded timestamps, so they will differ if any timestamp mapping was approximate.
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Test the workflow layer end to end
Fire every trigger, automation, SLA escalation, macro and notification with a live ticket. Workflow does not migrate — it gets rebuilt — so it is untested until someone has actually watched it run.
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Confirm compliance and produce the audit trail
Re-scan the loaded data for regulated fields, confirm retention and deletion policies are configured in Jira Service Management, and file the evidence with your PII decisions from the audit phase.
PII & Compliance Scanner Produce the compliance evidence your auditor will ask for -
Sign off, then decommission on a schedule
Get written acceptance against the Discovery success criteria. Keep Deskpro read-only for an agreed period (30-90 days is typical), take a final archive export, and only then cancel. Diarise the decommission date so it does not quietly renew.
Don't move on until
- Full reconciliation report attached to the project record
- Reporting baselines match pre-migration figures within agreed tolerance
- Formal acceptance signed and archive retention scheduled
Field mapping reference
The field-by-field mapping for each object. Use this as the starting point for your mapping spec.
Method Thread History
| Deskpro field | Jira Service Management field | Notes |
|---|---|---|
| CSV Import | No | No (public only) |
| API-Based Script | Yes | Yes |
| Third-Party Tool | Yes | Usually |
| Custom ETL Pipeline | Yes | Yes |
| Middleware (Zapier/Make) | Partial | Partial |
Object JSM Equivalent
| Deskpro field | Jira Service Management field | Notes |
|---|---|---|
| Ticket | Issue (Service Request) | One Deskpro ticket = one JSM issue. Must be assigned a specific Request Type so it appears correctly in the JSM customer portal. |
| Ticket Message (reply) | Comment (public) | Agent replies and user messages that were customer-visible. |
| Ticket Note | Comment (internal) | Restricted to "Service Desk Team" role in JSM. |
| Person (end user) | Customer | JSM customers are Atlassian accounts. Must be resolved to accountId, not email. |
| Organization | Organization | JSM organizations are project-scoped. A customer can belong to one organization per project. |
| Agent | Agent (licensed user) | Must exist in Atlassian before migration. |
| Department | Service Desk Project | One department typically maps to one JSM project. Can also map to Components within a single project. |
| Ticket Status | Issue Status (Workflow) | Requires workflow configuration. JSM issues must follow the assigned workflow — you cannot force an issue into "Closed" if the workflow requires it to pass through "In Progress" and "Resolved" first. |
| Ticket Priority | Priority | JSM has default priorities; can add custom ones. |
| Ticket Category | Labels or Custom Field | No direct equivalent; use labels or a select field. |
| Custom Ticket Field | Custom Field | Types must be matched (text→text, select→select). |
| Person Custom Field | Customer property | Very limited in JSM. Data may need to migrate to a linked Assets object or a custom field on issues. |
| Org Custom Field | Org property | Very limited in JSM. Consider using Assets. |
| SLA | SLA Policy | Must be rebuilt manually in JSM. No import path. |
| Knowledge Base Article | Confluence Page | Requires separate migration to Confluence. |
| Attachment | Attachment | Migrated per-issue via separate API call (multipart/form-data). |
| Tags/Labels | Labels | Direct mapping. |
| CCs/Participants | Request Participants | Requires accountIds, not emails. |
Deskpro JSM
| Deskpro field | Jira Service Management field | Notes |
|---|---|---|
| ticket.id | Custom field deskpro_ticket_id | JSM auto-generates issue keys (e.g., IT-101). Store Deskpro ID for traceability and delta re-runs. |
| ticket.subject | issue.summary | Max 255 characters in JSM. |
| ticket.status | issue.status | Map awaiting_user, awaiting_agent, resolved, closed to JSM workflow states. Requires executing workflow transitions in order. |
| ticket.priority | issue.priority | Map Deskpro priority values to JSM priority IDs. |
| person.primary_email | issue.reporter | Must be resolved to an Atlassian accountId via the user search API. Use email as the dedupe key. |
| agent.email | issue.assignee | Must be resolved to an accountId. |
| ticket.department_id | issue.project | Map department to JSM project. This should drive routing, not be stored as dead text. |
| ticket.date_created | issue.created | Read-only in JSM. Store original creation date in a custom field. Some migration services have workarounds to set historical dates. |
| ticket.labels | issue.labels | Normalize case and separators. |
| ticket.custom_fields.* | issue.customfield_* | Match field types carefully. Deskpro allows nested/hierarchical picklist choices; JSM supports single-select and cascading select (two levels only). Deep hierarchies must be flattened. |
| message.message | comment.body | HTML → ADF (v3 API) or wiki markup (v2 API). See conversion notes below. |
| message.is_note | comment.visibility | true → internal comment (role: "Service Desk Team"). |
| message.person_id | comment.author | Requires impersonation or explicit note about original author. |
| person.name | customer.displayName | Direct copy. |
| organization.name | organization.name | Create the org before loading shared requests. |
| attachment.filename | attachment.filename | Direct copy. Download from Deskpro then re-upload via multipart/form-data. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets | medium | Core ticket metadata maps reasonably well to JSM issues, but preserving original timestamps, status history, and multi-department assignments requires workarounds and careful API usage. |
| Conversation Threads | high | Deskpro's threaded message model with HTML bodies must be converted to ADF-formatted JSM comments, with CSV import offering no thread preservation and making all comments public. |
| Attachments | high | Each attachment requires individual API calls for both download from Deskpro and upload to JSM, with no bulk transfer mechanism and significant impact on migration runtime and rate limits. |
| Contacts (People) | high | Deskpro People records with rich custom fields must be resolved to Atlassian accountIds, and JSM's customer entity supports far fewer custom profile fields than Deskpro's person model. |
| Organizations | medium | Both platforms support organizations, but the customer-to-organization relationship model differs, and sites created after October 2025 may route organization features through Customer Service Management rather than JSM alone. |
| Custom Fields | high | Deskpro's per-ticket, per-person, and per-organization custom fields must be restructured into JSM's project-scoped, issue-type-scoped field model, with Deskpro's CSV export capping at approximately 55 fields. |
| Internal Notes | medium | Deskpro agent notes map to JSM internal comments, but CSV import makes all comments public, so only API-based migration can preserve the internal/public distinction. |
| SLA Policies | medium | Deskpro's per-department and per-organization SLA rules must be manually recreated as JSM SLA policies scoped to projects, with no automated migration path for SLA configurations. |
| Knowledge Base Articles | high | Deskpro's built-in KB content must be migrated to Confluence as a separate workstream, requiring HTML-to-Confluence-storage-format conversion and permission model redesign. |
| Departments and Agent Groups | medium | Deskpro departments map to JSM projects and queues but the mapping is not one-to-one, requiring decisions about project structure and potentially fanning out departments across multiple service projects. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
HTML to ADF Conversion
Deskpro stores ticket messages as HTML content, which must be programmatically converted to Atlassian Document Format (ADF) required by JSM's v3 REST API, a non-trivial transformation that can silently drop formatting or inline content.
User Identity Resolution
Deskpro identifies users via email-based People records, while JSM requires Atlassian accountIds, necessitating a lookup and mapping layer to correctly attribute tickets, comments, and customer relationships.
Conversation Thread Model Mismatch
Deskpro models conversations as multi-message threads with distinct reply types (agent_reply, user reply, note), whereas JSM uses a flat comments model with only public and internal visibility flags, requiring careful type mapping.
Custom Field Scope Differences
Deskpro supports custom fields at the ticket, person, and organization levels independently, while JSM scopes custom fields per issue type within a project, requiring schema redesign and potential data consolidation.
Archived and Inactive Ticket Extraction
Deskpro's API returns only active tickets by default, so resolved and archived tickets must be explicitly requested with status filters to avoid silently omitting historical data during extraction.
Knowledge Base Platform Separation
Deskpro includes a built-in knowledge base, but JSM relies on Confluence as a separate product for KB content, requiring an additional migration workstream with its own content transformation and permission mapping.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate Deskpro to Jira Service Management using CSV export?
Only partially. Deskpro's CSV export captures ticket metadata but does not include full conversation threads, attachments, or customer–organization relationships. Deskpro reports also have a ~55-field limit. JSM CSV imports make all comments public, so you lose internal/private note distinctions. For anything beyond basic ticket data, you need an API-based migration.
What are the API rate limits for Deskpro and JSM during migration?
Deskpro Cloud has a default global rate limit configurable per API key; contact support@deskpro.com to raise it before a migration. JSM Cloud enforces burst rate limits per-tenant per-API — exceeding them returns a 429 error with a Retry-After header. API token-based traffic uses burst limits, not the newer points-based system introduced in March 2026.
How long does a Deskpro to JSM migration take?
Timelines depend on volume. Under 10K tickets with minimal attachments: 3–5 business days. 10K–100K tickets with attachments: 1–3 weeks. Over 100K tickets: 3–6 weeks. These estimates include test runs, validation, and cutover.
Does Jira Service Management have per-issue limits that affect migration?
Yes. Jira Cloud enforces per-issue limits: 5,000 comments, 2,000 attachments, 2,000 issue links, and 2,000 remote links per issue. Deskpro tickets with very long conversation threads exceeding 5,000 messages may need to be split across multiple linked JSM issues.
How do I prevent JSM from sending emails to customers during the migration?
Disable outgoing email notifications in your Jira Service Management project settings before running the migration script. Otherwise, customers will receive notification emails for every historical ticket being recreated. Re-enable notifications only after migration and validation are complete.